



'''simple docstring'''





from __future__ import annotations

from fractions import Fraction




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     return (
         num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
     )




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    []
     _a           =    11
     _a           =    int('''1''' + '''0''' * digit_len)
     for num in range(__A    ,      __A):
          while den <= 99:
               if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                    if is_digit_cancelling(__A    ,      __A):
                         solutions.append(F'''{num}/{den}''')
               den += 1
          num += 1
          _a           =    10
     return solutions




def lowerCAmelCase    (__A = 2):



     """simple docstring"""



     _a           =    1.0
     for fraction in fraction_list(__A):
          _a           =    Fraction(__A)
          result *= frac.denominator / frac.numerator
     return int(__A)


if __name__ == "__main__":
  print(solution())








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a      :  List[str]			     =			logging.get_logger(__name__)


if is_vision_available():
	import PIL







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = True,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""shortest_edge""": 224}
						UpperCAmelCase_:		int							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   default_to_square=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Union[str, Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   default_to_square=SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		Optional[int]							      =     do_resize
						UpperCAmelCase_:		Optional[int]							      =     size
						UpperCAmelCase_:		Optional[int]							      =     resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop
						UpperCAmelCase_:		Optional[int]							      =     crop_size
						UpperCAmelCase_:		str							      =     do_rescale
						UpperCAmelCase_:		Optional[int]							      =     rescale_factor
						UpperCAmelCase_:		List[str]							      =     do_normalize
						UpperCAmelCase_:		Any							      =     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else OPENAI_CLIP_STD
						UpperCAmelCase_:		Any							      =     do_convert_rgb

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   default_to_square=SCREAMING_SNAKE_CASE_					)
						if "shortest_edge" not in size:
									raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'					)
						UpperCAmelCase_:		Optional[Any]							      =     get_resize_output_image_size(SCREAMING_SNAKE_CASE_,   size=size["""shortest_edge"""],   default_to_square=SCREAMING_SNAKE_CASE_					)
						return resize(SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Any							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   Union[str, Any]:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Tuple							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Tuple							      =     size if size is not None else self.size
						UpperCAmelCase_:		str							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""size""",   default_to_square=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Optional[int]							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		int							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size""",   default_to_square=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		int							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		Tuple							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else self.image_std
						UpperCAmelCase_:		Union[str, Any]							      =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						UpperCAmelCase_:		List[str]							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None:
									raise ValueError("""Size must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
									UpperCAmelCase_:		Any							      =     [convert_to_rgb(SCREAMING_SNAKE_CASE_					) for image in images]

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		List[str]							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		str							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[Any]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		Dict							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Tuple							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Union[str, Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






